122 lines
5.0 KiB
Python
122 lines
5.0 KiB
Python
# -*- coding: utf-8 -*-
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"""
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阿里云 DashScope（通义千问）提取面板结构化数据
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从新闻文本中提取战损、报复指数、基地状态等，供 db_merge 落库
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API Key 通过环境变量 DASHSCOPE_API_KEY 配置
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"""
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import json
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import os
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import re
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from datetime import datetime, timezone
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from typing import Any, Dict, Optional
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from panel_schema import validate_category, validate_severity, validate_summary
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def _call_dashscope_extract(text: str, timeout: int = 15) -> Optional[Dict[str, Any]]:
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"""调用阿里云 DashScope 提取结构化数据"""
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api_key = os.environ.get("DASHSCOPE_API_KEY", "").strip()
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if not api_key or not text or len(str(text).strip()) < 10:
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return None
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try:
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import dashscope
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from http import HTTPStatus
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dashscope.api_key = api_key
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prompt = f"""从以下美伊/中东军事新闻中提取可明确推断的数值，输出 JSON。无依据的字段省略不写。
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要求：
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- summary: 1-2句中文事实摘要，≤80字
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- category: deployment|alert|intel|diplomatic|other
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- severity: low|medium|high|critical
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- 战损（仅当新闻明确提及数字时填写）:
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us_personnel_killed, iran_personnel_killed, us_personnel_wounded, iran_personnel_wounded,
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us_civilian_killed, iran_civilian_killed, us_civilian_wounded, iran_civilian_wounded,
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us_bases_destroyed, iran_bases_destroyed, us_bases_damaged, iran_bases_damaged,
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us_aircraft, iran_aircraft, us_warships, iran_warships, us_armor, iran_armor, us_vehicles, iran_vehicles
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- retaliation_sentiment: 0-100，仅当新闻涉及伊朗报复/反击情绪时
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- wall_street_value: 0-100，仅当新闻涉及美股/市场反应时
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- key_location_updates: 当新闻提及具体基地遭袭时，数组 [{{"name_keywords":"阿萨德|asad|assad","side":"us","status":"attacked","damage_level":1-3}}]
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原文：
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{str(text)[:800]}
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直接输出 JSON，不要其他解释："""
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response = dashscope.Generation.call(
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model="qwen-turbo",
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messages=[{"role": "user", "content": prompt}],
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result_format="message",
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max_tokens=512,
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)
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if response.status_code != HTTPStatus.OK:
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return None
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raw = (response.output.get("choices", [{}])[0].get("message", {}).get("content", "") or "").strip()
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raw = re.sub(r"^```\w*\s*|\s*```$", "", raw)
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return json.loads(raw)
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except Exception:
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return None
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def extract_from_news(text: str, timestamp: Optional[str] = None) -> Dict[str, Any]:
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"""
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从新闻文本提取结构化数据，符合面板 schema
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返回: { situation_update?, combat_losses_delta?, retaliation?, wall_street?, key_location_updates? }
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"""
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ts = timestamp or datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%S.000Z")
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out: Dict[str, Any] = {}
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parsed = _call_dashscope_extract(text)
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if not parsed:
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return out
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if parsed.get("summary"):
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out["situation_update"] = {
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"summary": validate_summary(str(parsed["summary"])[:120], 120),
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"category": validate_category(str(parsed.get("category", "other")).lower()),
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"severity": validate_severity(str(parsed.get("severity", "medium")).lower()),
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"timestamp": ts,
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}
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loss_us = {}
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loss_ir = {}
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for k in ["personnel_killed", "personnel_wounded", "civilian_killed", "civilian_wounded",
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"bases_destroyed", "bases_damaged", "aircraft", "warships", "armor", "vehicles"]:
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uk, ik = f"us_{k}", f"iran_{k}"
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if uk in parsed and isinstance(parsed[uk], (int, float)):
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loss_us[k] = max(0, int(parsed[uk]))
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if ik in parsed and isinstance(parsed[ik], (int, float)):
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loss_ir[k] = max(0, int(parsed[ik]))
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if loss_us or loss_ir:
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out["combat_losses_delta"] = {}
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if loss_us:
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out["combat_losses_delta"]["us"] = loss_us
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if loss_ir:
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out["combat_losses_delta"]["iran"] = loss_ir
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if "retaliation_sentiment" in parsed:
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v = parsed["retaliation_sentiment"]
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if isinstance(v, (int, float)) and 0 <= v <= 100:
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out["retaliation"] = {"value": int(v), "time": ts}
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if "wall_street_value" in parsed:
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v = parsed["wall_street_value"]
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if isinstance(v, (int, float)) and 0 <= v <= 100:
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out["wall_street"] = {"time": ts, "value": int(v)}
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if "key_location_updates" in parsed and isinstance(parsed["key_location_updates"], list):
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valid = []
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for u in parsed["key_location_updates"]:
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if isinstance(u, dict) and u.get("name_keywords") and u.get("side") in ("us", "iran"):
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valid.append({
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"name_keywords": str(u["name_keywords"]),
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"side": u["side"],
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"status": str(u.get("status", "attacked"))[:20],
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"damage_level": min(3, max(1, int(u["damage_level"]))) if isinstance(u.get("damage_level"), (int, float)) else 2,
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})
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if valid:
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out["key_location_updates"] = valid
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return out
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